Literature DB >> 20601773

Computational simulation of breast compression based on segmented breast and fibroglandular tissues on magnetic resonance images.

Tzu-Ching Shih1, Jeon-Hor Chen, Dongxu Liu, Ke Nie, Lizhi Sun, Muqing Lin, Daniel Chang, Orhan Nalcioglu, Min-Ying Su.   

Abstract

This study presents a finite element-based computational model to simulate the three-dimensional deformation of a breast and fibroglandular tissues under compression. The simulation was based on 3D MR images of the breast, and craniocaudal and mediolateral oblique compression, as used in mammography, was applied. The geometry of the whole breast and the segmented fibroglandular tissues within the breast were reconstructed using triangular meshes by using the Avizo 6.0 software package. Due to the large deformation in breast compression, a finite element model was used to simulate the nonlinear elastic tissue deformation under compression, using the MSC.Marc software package. The model was tested in four cases. The results showed a higher displacement along the compression direction compared to the other two directions. The compressed breast thickness in these four cases at a compression ratio of 60% was in the range of 5-7 cm, which is a typical range of thickness in mammography. The projection of the fibroglandular tissue mesh at a compression ratio of 60% was compared to the corresponding mammograms of two women, and they demonstrated spatially matched distributions. However, since the compression was based on magnetic resonance imaging (MRI), which has much coarser spatial resolution than the in-plane resolution of mammography, this method is unlikely to generate a synthetic mammogram close to the clinical quality. Whether this model may be used to understand the technical factors that may impact the variations in breast density needs further investigation. Since this method can be applied to simulate compression of the breast at different views and different compression levels, another possible application is to provide a tool for comparing breast images acquired using different imaging modalities--such as MRI, mammography, whole breast ultrasound and molecular imaging--that are performed using different body positions and under different compression conditions.

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Year:  2010        PMID: 20601773      PMCID: PMC2993491          DOI: 10.1088/0031-9155/55/14/013

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  38 in total

1.  Factors influencing the accuracy of biomechanical breast models.

Authors:  Christine Tanner; Julia A Schnabel; Derek L G Hill; David J Hawkes; Martin O Leach; D Rodney Hose
Journal:  Med Phys       Date:  2006-06       Impact factor: 4.071

2.  A finite element model to accurately predict real deformations of the breast.

Authors:  A Pérez del Palomar; B Calvo; J Herrero; J López; M Doblaré
Journal:  Med Eng Phys       Date:  2008-03-10       Impact factor: 2.242

3.  Invited commentary: assessing breast density change--lessons for future studies.

Authors:  Celia Byrne
Journal:  Am J Epidemiol       Date:  2008-04-02       Impact factor: 4.897

4.  The importance of organ geometry and boundary constraints for planning of medical interventions.

Authors:  S Misra; K J Macura; K T Ramesh; A M Okamura
Journal:  Med Eng Phys       Date:  2008-09-23       Impact factor: 2.242

5.  An automated approach for estimation of breast density.

Authors:  John J Heine; Michael J Carston; Christopher G Scott; Kathleen R Brandt; Fang-Fang Wu; Vernon Shane Pankratz; Thomas A Sellers; Celine M Vachon
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2008-11       Impact factor: 4.254

6.  A pilot study of compositional analysis of the breast and estimation of breast mammographic density using three-dimensional T1-weighted magnetic resonance imaging.

Authors:  Michael Khazen; Ruth M L Warren; Caroline R M Boggis; Emilie C Bryant; Sadie Reed; Iqbal Warsi; Linda J Pointon; Gek E Kwan-Lim; Deborah Thompson; Ros Eeles; Doug Easton; D Gareth Evans; Martin O Leach
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2008-09       Impact factor: 4.254

7.  A new method for quantitative analysis of mammographic density.

Authors:  Carri K Glide-Hurst; Neb Duric; Peter Littrup
Journal:  Med Phys       Date:  2007-11       Impact factor: 4.071

8.  Relationships between circulating hormone levels, mammographic percent density and breast cancer risk factors in postmenopausal women.

Authors:  Harriet Johansson; Sara Gandini; Bernardo Bonanni; Frederique Mariette; Aliana Guerrieri-Gonzaga; Davide Serrano; Enrico Cassano; Francesca Ramazzotto; Laura Baglietto; Maria Teresa Sandri; Andrea Decensi
Journal:  Breast Cancer Res Treat       Date:  2007-04-28       Impact factor: 4.872

Review 9.  Mammographic density. Potential mechanisms of breast cancer risk associated with mammographic density: hypotheses based on epidemiological evidence.

Authors:  Lisa J Martin; Norman F Boyd
Journal:  Breast Cancer Res       Date:  2008-01-09       Impact factor: 6.466

10.  Comparison of a new and existing method of mammographic density measurement: intramethod reliability and associations with known risk factors.

Authors:  Valerie A McCormack; Ralph Highnam; Nicholas Perry; Isabel dos Santos Silva
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2007-06       Impact factor: 4.254

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  6 in total

1.  Automated detection of mass lesions in dedicated breast CT: a preliminary study.

Authors:  I Reiser; R M Nishikawa; M L Giger; J M Boone; K K Lindfors; K Yang
Journal:  Med Phys       Date:  2012-02       Impact factor: 4.071

2.  Finite-element modeling of compression and gravity on a population of breast phantoms for multimodality imaging simulation.

Authors:  Gregory M Sturgeon; Nooshin Kiarashi; Joseph Y Lo; E Samei; W P Segars
Journal:  Med Phys       Date:  2016-05       Impact factor: 4.071

3.  A Regression Model for Predicting Shape Deformation after Breast Conserving Surgery.

Authors:  Hooshiar Zolfagharnasab; Sílvia Bessa; Sara P Oliveira; Pedro Faria; João F Teixeira; Jaime S Cardoso; Hélder P Oliveira
Journal:  Sensors (Basel)       Date:  2018-01-09       Impact factor: 3.576

4.  An Iterative Method for Estimating Nonlinear Elastic Constants of Tumor in Soft Tissue from Approximate Displacement Measurements.

Authors:  Maryam Mehdizadeh Dastjerdi; Ali Fallah; Saeid Rashidi
Journal:  J Healthc Eng       Date:  2019-01-06       Impact factor: 2.682

5.  Relation between Microstructures and Macroscopic Mechanical Properties of Earthen-Site Soils.

Authors:  Yingmin Zhang; Guang Yang; Wenwu Chen; Lizhi Sun
Journal:  Materials (Basel)       Date:  2022-09-03       Impact factor: 3.748

6.  An Anthropometric-Based Subject-Specific Finite Element Model of the Human Breast for Predicting Large Deformations.

Authors:  Silvia Pianigiani; Leonardo Ruggiero; Bernardo Innocenti
Journal:  Front Bioeng Biotechnol       Date:  2015-12-24
  6 in total

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